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An artificial neural network-source apportionment-based prediction model for carbon monoxide from total number of ships calling by ports in Malaysia

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Abstract

Air pollution has been a significant issue in recent years due to rising industrialization and maritime activity around the globe, making air pollution forecasting a crucial concept in environmental study. This prompted the deployment of principal component analysis (PCA) for the source apportionment amongst the air quality parameters and the artificial neural network (ANN) for the prediction of the significant air quality parameters in ports area for this study. The study was carried out in seven federal ports across Malaysia for the period of 2009 and 2018, and 14 air quality parameters were calculated using information on air quality acquired from the Department of the Environment. The results of the study showed PCA identified NOx, NO, SO, NO2, CO, and PM10 as the variables of significance with a variation of 44.31% with CO exhibiting the highest factor loading (0.968). Artificial Neural Network-Source Apportionment accurately predicted CO as the major pollutant with R2 in training (0.7492) and validation (0.7492). This study has successfully established a connection between the source of apportionment of air pollutant parameters and the total number of ships, as well as an effective alternative tool for predicting the most significant air quality air pollutant parameters in Malaysian ports, which can be applied in other regions to comprehend ship emission trends.

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Data availability

The air quality datasets generated during and/or analysed during the current study were acquired from Department of Environment Malaysia and they have not given their permission for researchers to share their data.

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Acknowledgements

The authors would like to thank the Department of the Environment Malaysia (DOE) for their permission to utilise the air quality data and Department of Statistics Malaysia (DOS) provides a list of information for all ships that visited Malaysian ports between 2009 and 2018. Author contribution: M. S. Samsudin: Supervision, Project administration, Writing – original draft, Visualisation, writing-review & editing, Conceptualization, Methodology, Software, Validation, Formal analysis. A. Azid: Conceptualization, Methodology, Formal Analysis, Validation, Software, Writing – review & editing. N. L. A. Rani: Data curation, Writing – review & editing. M. A. Zaudi: Mapping, Visualisation, Data curation. S. M. Shaharudin: Validation, Formal analysis, Data curation. M. L. Tan: Validation, Formal analysis, Data curation. I. B. Koki: Writing – review & editing

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Authors and Affiliations

  1. Environmental Technology Division, School of Industrial Technology, Universiti Sains Malaysia, 11800, Penang, Malaysia

    Mohd Saiful Samsudin

  2. Renewable Biomass Transformation Cluster, School of Industrial Technology, Universiti Sains Malaysia, 11800, Penang, Malaysia

    Mohd Saiful Samsudin

  3. Faculty of Bioresources and Food Industry, Universiti Sultan Zainal Abidin, Besut Campus, 22200, Besut, Terengganu, Malaysia

    Azman Azid

  4. Faculty of Applied Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Nurul Latiffah Abd Rani

  5. Department of Environment, Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 UPM, Serdang, Malaysia

    Muhammad Amar Zaudi

  6. Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia

    Shazlyn Millenana Saharuddin

  7. GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, Penang, Malaysia

    Mou Leong Tan

  8. Department of Chemistry, Yusuf Maitama Sule University, Kano, PMB 3220, Kano, Nigeria

    Isa Baba Koki

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Samsudin, M.S., Azid, A., Rani, N.L.A. et al. An artificial neural network-source apportionment-based prediction model for carbon monoxide from total number of ships calling by ports in Malaysia. Neural Comput & Applic 36, 11323–11337 (2024). https://doi.org/10.1007/s00521-024-09699-7

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